Long-term multi-animal tracking in densely group-housed agricultural settings is critical for automated behavior monitoring and early anomaly detection in precision livestock farming. However, it poses significant challenges due to persistent occlusions from feeders and water dispensers, high inter-individual appearance similarity, and drastic visual changes across day and night cycles. Existing multi-object tracking datasets rarely capture the combined difficulty of these real-world conditions. To address this, we introduce OinkTrack, a large-scale benchmark for continuous multi-pig tracking in commercial farm environments. The dataset comprises over five hours of annotated video across sixteen sequences, covering day, night, night-to-day, and day-to-night transitions. Each sequence ranges from one minute to one hour, featuring an average of thirty-six pigs per frame. In total, OinkTrack provides 573,700 bounding boxes linked to 574 consistent pig identities. It enables detailed behavior analysis under varying lighting and crowding conditions. We describe the data collection and annotation process, present statistical insights into tracking difficulty, and benchmark 11 state-of-the-art tracking methods. OinkTrack provides a robust foundation for developing long-term tracking models and supports downstream applications such as individual activity profiling and early detection of abnormal behavior in real-world, high-density animal populations. The complete dataset and supplementary materials are publicly accessible at https://leohuang0511.github.io/oinktrack-page.

A digital twin (DT) is a virtual system that represents a physical system. By simulating the behavior of a digital twin in what-if conditions, the operators of a physical system can predict the outcome of the physical system in such conditions and take advance measures to improve its performance. In this work, we built the digital twin of a large and high-speed network of an Internet Service Provider (ISP) in Taiwan and used simulations to predict how the packet loss rates of its flows would change after physical link failures, under the protection of Link Aggregation Group (LAG). To do so, we first used sFlow to collect the data of the flows traversing the network and then used the flow data to conduct the simulations. This paper presents the methodology that we developed to simulate a Network Digital Twin (NDT), its simulation results, and the insights that we obtained from this work.

Due to the burstiness property of compressed videos, an effective video traffic description scheme is important for efficient network bandwidth management. This paper proposes an elastically deterministic video traffic regulating mechanism (ED-VTR) that models and regulates the video traffic based on constraints of available decoder buffer space. In contrast to traditional methods, ED-VTR generates a transmission schedule that contains a series of monotonically decreasing bandwidth requests. Simulation results show that, using lower initial bandwidth and smaller decoder buffer requirement, ED-VTR can effectively reduce the complexity of bandwidth management and still hold the advantages of traditional traffic smoothing and deterministic traffic modeling methods.